将Pandas系列插入DataFrame可使所有值变为Nan

Question

As the title implies I have converted two dictionaries into series like so and I tried to insert them into the data frame df.

first_series = pd.Series(first_dict, name='State Names')
second_series = pd.Series(second_dict, name='City Names')
column_loc=list(df.columns.values).index("ipAddr")
df.insert(column_loc+1, 'State Names', first_series)
df.insert(column_loc+2, 'City Names', second_series)

When I run this however I get

              ipAddr State Names City Names    ...       
respID                                         ...        
10018         ***.**.**.**  NaN        NaN     ...        
10025         **.**.**.**   NaN        NaN     ...       

the series are as follows

10018       Bedford
10025     Vancouver
        ...    
10267        Lompoc
10280    Pikesville
Name: State Names, dtype: object
--------------------------------------------------------
10018          Ohio
10025    Washington
        ...    
10267    California
10280      Maryland
Name: City Names, dtype: object

I've checked that both the dictionaries and the resulting series are populated so I don't understand why this is occurring.

Thank you.

edit: A similar question got asked here but wasn't answered When I insert pandas Series into dataframe, all values become NaN

Answer 1

It turned out the Series has less rows than the df. Also, Series.values need to be used while assigning it to a df column.